Lead, Data Engineer

Summary

The Lead Data Engineer is a senior individual contributor within McKesson’s Decision Intelligence organization, responsible for leading the design, delivery, and operationalization of scalable data engineering and analytics solutions across the PSaS Business Unit. This role serves as a technical leader and player‑coach, combining deep hands‑on engineering with solution leadership, mentorship, and cross‑functional influence.

What You’ll Do

• Lead end‑to‑end technical delivery for complex data engineering initiatives or domains.
• Serve as a senior technical point of contact for data engineering within cross‑functional squads.
• Design and build scalable, reliable batch and real‑time data pipelines across internal and external systems.
• Lead solution design and influence architectural decisions in collaboration with Data Architects and enterprise teams.
• Define and uphold engineering standards, patterns, and best practices aligned with enterprise data strategy.
• Mentor and guide data engineers through design reviews, code reviews, and technical coaching.
• Ensure quality, reliability, performance, and long‑term maintainability of data products.
• Partner with Product Managers, Data Scientists, Analysts, and business stakeholders to translate analytical requirements into scalable solutions.
• Drive an automation‑first and reusability‑focused mindset across data engineering solutions.
• Oversee testing strategies, production readiness, observability, and operational stability of data pipelines.
• Proactively identify technical debt and lead remediation efforts.
• Enable advanced analytics and ML use cases through optimized data models and pipelines.
• Communicate technical designs, tradeoffs, risks, and outcomes clearly to stakeholders.

What You Bring

• Recognized expertise in data engineering and analytics within large, enterprise environments.
• Strong ability to independently lead complex technical initiatives with minimal oversight.
• Proven experience influencing technical direction and mentoring other engineers without formal people‑management responsibility.
• Deep understanding of data architecture, ETL/ELT patterns, and large‑scale data processing.
• Strong stakeholder communication and collaboration skills.

Technical Skills

• Advanced proficiency in SQL, Python, and scripting.
• Strong hands‑on experience with: Databricks, Snowflake, Azure Data FactoryConfluent Kafka / Azure Event HubPySpark and analytical SQLPower BI and/or TableauApache Airflow, dbt, Alation
• Strong understanding of data modeling, metadata, lineage, and data quality practices.
• Experience with cloud platforms (SaaS, PaaS, IaaS).
• Experience implementing automation and Infrastructure as Code (IaC).
• Ability to support advanced analytics and ML enablement (non‑pure DS focus).

Minimum Requirements

• Degree or equivalent and typically requires 10+ years of relevant experience.

Preferable Skills & Experience

• Experience leading enterprise‑scale or BU‑wide data initiatives.
• Exposure to real‑time, event‑driven data architectures.
• Familiarity with data governance programs and stewardship responsibilities.
• Experience experimenting with emerging data and AI technologies.
• Background in highly regulated or complex business environments.

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please

Our Base Pay Range for this position

$122,100 - $162,800

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